In the practice of electronic color imaging, it is desirable to simultaneously capture image data in sets of three color planes, usually red, green, and blue. When the three color planes are combined, it is possible to create a high-quality color image. Capturing these three sets of image data can be done in a number of ways. Ideally, three separate two-dimensional image sensors would be used, each capturing one color plane. However, in the interest of conserving space and cost, as well as minimizing complexity, image data in three colors is ordinarily captured with a single two-dimensional sensor; such a sensor includes an array of photosensitive pixels that are covered by a pattern of red, green, and blue filters. This type of filter pattern is known as a color filter array (CFA), and the resulting image is referred to hereafter as a CFA image. FIG. 1 shows how the red (R), green (G), and blue (B) pixels are commonly arranged in a CFA pattern on the surface of an image sensor. The illustrated pattern is the well known Bayer array, which is described in U.S. Pat. No. 3,971,065, incorporated herein by reference. In particular, note that each photosensitive pixel produces a single color signal.
When a CFA image is obtained from a sensor using a CFA pattern, it is therefore necessary to interpolate the missing red, green, and blue values so that there is an estimate of all three color values for each pixel location. Once the interpolation is completed, each pixel is associated with three color values and therefore can be processed by a variety of known image processing techniques, depending on the needs of the system. Some examples of such processing include image sharpening, color correction, or halftoning.
An electronic camera of this type is described in patent application Ser. No. 085,516,entitled "Single Sensor Color Camera with User-Selectable Image Record Size", filed Jun. 30, 1993 in the name of the same assignee as the present application. Furthermore, application Ser. No. 085,516 describes an electronic camera adapted for processing images of different resolution in order to provide a user selectable image record size. A buffer memory is provided for storing color image pixels from a sensor, and a timing controller responsive to a resolution mode switch controls the order in which color image pixels are selected for storage in both vertical and horizontal directions. The order selected by the resolution mode switch includes a full resolution mode, and at least one reduced resolution mode in which the color image pixels are subsampled. FIG. 2A shows an example of one such low-resolution pattern, with a circle surrounding each sampled pixel.
The electronic camera described in Ser. No. 085,516 is capable of storing full resolution CFA images or reduced resolution images, or a combination of images of the different resolutions. Though several methods of generating the low resolution image are described, all of the methods have the same disadvantage. They all create a low resolution CFA image that is spatially different from the original CFA image. Even in FIG. 2B, which shows another low resolution CFA image, the red pixels and the blue pixels are not centered with respect to the green pixels. Consequently, interpolated low resolution pixels are calculated using pixels that are not centered in the same geometric pattern as the original CFA image. This happens because it is difficult to find a subsampling pattern for the Bayer array that would replicate the geometric spacing of the original CFA pattern, at least for moderate levels of resolution reduction. The shifted geometry of the reduced resolution pixels has the following disadvantages.
1. The CFA interpolation is designed for the full resolution CFA geometry. If the interpolation is applied to the shifted geometry of the low resolution pixels, e.g. as shown in either FIG. 2A or FIG. 2B, it will cause artifacts in the image. To avoid this, it is necessary to implement a separate, and typically more complicated, interpolation algorithm.
2. If the image is stored in a compressed form, the compression algorithm may not be optimal for the low resolution pixel geometries. This can cause the compression algorithm to compress at a higher bit rate or lower quality. For example, unwanted color shadows and color fringing can show up in the subsequently decompressed image.
3. If further resolution reduction is necessary, each new reduction shifts the geometry again, so that new interpolation algorithms must be designed.